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Iteration 220: Add sem_var and nunique/any/all
Add stats/sem_var.ts: varSeries/varDataFrame (sample/population variance, configurable ddof/skipna/minCount/axis/numericOnly) and semSeries/semDataFrame (SEM = sqrt(var/n)). StatFn type alias for clean reducer callbacks. 25 unit tests + 3 property tests. Add stats/nunique.ts: nuniqueSeries/nuniqueDataFrame (count unique values, dropna), anySeries/allSeries (boolean reductions, skipna, vacuous all), anyDataFrame/allDataFrame (axis, skipna, boolOnly). Extract anyInSlice/allInSlice/rowValues helpers to keep complexity under 15. 31 unit tests + 2 property tests. Playground: sem_var.html, nunique.html. Update playground/index.html. Metric: 55 (+2 from 53 actual baseline, beats best_metric 54). Run: https://github.com/githubnext/tsessebe/actions/runs/24299079452 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎playground/index.html‎

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@@ -389,6 +389,16 @@ <h3><a href="skew_kurt.html" style="color: var(--accent); text-decoration: none;
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<p>Adjusted Fisher–Pearson skewness and bias-corrected excess kurtosis for Series and DataFrame. Supports <code>skipna</code>, <code>axis</code>, and <code>numericOnly</code>. Mirrors <code>Series.skew()</code> / <code>Series.kurt()</code>.</p>
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<div class="status done">✅ Complete</div>
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</div>
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<div class="feature-card">
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<h3><a href="sem_var.html" style="color: var(--accent); text-decoration: none;">📊 var / sem</a></h3>
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<p>Sample/population variance (<code>varSeries</code>) and standard error of the mean (<code>semSeries</code>) for Series and DataFrame. Configurable <code>ddof</code>, <code>skipna</code>, <code>minCount</code>, and <code>axis</code>. Mirrors <code>Series.var()</code> / <code>Series.sem()</code>.</p>
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<div class="status done">✅ Complete</div>
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</div>
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<div class="feature-card">
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<h3><a href="nunique.html" style="color: var(--accent); text-decoration: none;">🔢 nunique / any / all</a></h3>
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<p>Count unique values (<code>nuniqueSeries</code>, <code>nuniqueDataFrame</code>) and boolean reductions (<code>anySeries</code>, <code>allSeries</code>, <code>anyDataFrame</code>, <code>allDataFrame</code>). Supports <code>dropna</code>, <code>skipna</code>, <code>axis</code>, and <code>boolOnly</code>.</p>
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<div class="status done">✅ Complete</div>
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</div>
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</div>
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</section>
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</main>

‎playground/nunique.html‎

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>tsb — nunique / any / all</title>
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<style>
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body { font-family: system-ui, sans-serif; max-width: 900px; margin: 40px auto; padding: 0 20px; background: #f9fafb; color: #1a1a2e; }
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h1 { color: #4f46e5; }
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h2 { color: #374151; border-bottom: 2px solid #e5e7eb; padding-bottom: 6px; }
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.demo { background: white; border: 1px solid #e5e7eb; border-radius: 8px; padding: 20px; margin: 16px 0; }
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pre { background: #1e1e2e; color: #cdd6f4; padding: 16px; border-radius: 6px; overflow-x: auto; font-size: 14px; }
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.output { background: #f0fdf4; border: 1px solid #bbf7d0; border-radius: 6px; padding: 12px; font-family: monospace; white-space: pre; margin-top: 10px; }
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a { color: #4f46e5; }
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</style>
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</head>
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<body>
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<p><a href="index.html">← tsb playground</a></p>
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<h1>🔢 nunique / any / all</h1>
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<p>
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Count unique values and perform boolean reductions, mirroring
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.nunique.html"><code>Series.nunique()</code></a>,
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html"><code>Series.any()</code></a>, and
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.all.html"><code>Series.all()</code></a>.
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</p>
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<h2>1 · nunique — count distinct values</h2>
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<div class="demo">
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<pre>import { Series, nuniqueSeries } from "tsb";
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const s = new Series({ data: [1, 2, 2, 3, 3, 3, null] });
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nuniqueSeries(s); // 3 (null excluded by default)
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nuniqueSeries(s, { dropna: false }); // 4 (null counted as a distinct value)</pre>
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<div class="output">nuniqueSeries(s) → 3
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nuniqueSeries(s, {dropna:false}) → 4</div>
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</div>
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<h2>2 · any — is any element truthy?</h2>
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<div class="demo">
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<pre>import { anySeries } from "tsb";
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const allZero = new Series({ data: [0, 0, 0] });
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const hasOne = new Series({ data: [0, 0, 1] });
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anySeries(allZero); // false
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anySeries(hasOne); // true
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// With nulls (skipna=true by default)
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const withNull = new Series({ data: [null, 0, null] });
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anySeries(withNull); // false — null skipped, 0 is falsy</pre>
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<div class="output">anySeries(allZero) → false
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anySeries(hasOne) → true
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anySeries(withNull) → false</div>
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</div>
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<h2>3 · all — are all elements truthy?</h2>
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<div class="demo">
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<pre>import { allSeries } from "tsb";
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const allTrue = new Series({ data: [1, 2, 3] });
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const hasFalsy = new Series({ data: [1, 0, 3] });
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allSeries(allTrue); // true
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allSeries(hasFalsy); // false
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// Empty or all-null series vacuously returns true
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allSeries(new Series({ data: [] })); // true
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allSeries(new Series({ data: [null, null] })); // true</pre>
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<div class="output">allSeries(allTrue) → true
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allSeries(hasFalsy) → false
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allSeries([]) → true (vacuous)
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allSeries([null]) → true (vacuous)</div>
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</div>
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<h2>4 · DataFrame nunique</h2>
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<div class="demo">
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<pre>import { DataFrame, nuniqueDataFrame } from "tsb";
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const df = DataFrame.fromColumns({
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category: ["A", "B", "A", "C"],
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value: [1, 2, 1, 3 ],
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});
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nuniqueDataFrame(df); // per-column: category→3, value→3
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nuniqueDataFrame(df, { axis: 1 }); // per-row: how many distinct values in each row</pre>
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<div class="output">nuniqueDataFrame(df) → category: 3, value: 3
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nuniqueDataFrame(df, {axis:1}) → row0: 2, row1: 2, row2: 2, row3: 2</div>
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</div>
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<h2>5 · DataFrame any / all</h2>
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<div class="demo">
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<pre>import { anyDataFrame, allDataFrame } from "tsb";
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const df2 = DataFrame.fromColumns({
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a: [0, 0, 1],
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b: [1, 1, 1],
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});
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anyDataFrame(df2); // a: true, b: true (each col has at least one truthy)
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allDataFrame(df2); // a: false, b: true (col a has a 0)
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// axis=1: reduce across columns per row
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anyDataFrame(df2, { axis: 1 }); // row0: true, row1: true, row2: true
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allDataFrame(df2, { axis: 1 }); // row0: false, row1: false, row2: true</pre>
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<div class="output">anyDataFrame(df2) → a: true, b: true
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allDataFrame(df2) → a: false, b: true
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anyDataFrame(df2,{axis:1}) → [true, true, true]
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allDataFrame(df2,{axis:1}) → [false, false, true]</div>
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</div>
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</body>
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</html>

‎playground/sem_var.html‎

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>tsb — sem_var</title>
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<style>
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body { font-family: system-ui, sans-serif; max-width: 900px; margin: 40px auto; padding: 0 20px; background: #f9fafb; color: #1a1a2e; }
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h1 { color: #4f46e5; }
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h2 { color: #374151; border-bottom: 2px solid #e5e7eb; padding-bottom: 6px; }
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.demo { background: white; border: 1px solid #e5e7eb; border-radius: 8px; padding: 20px; margin: 16px 0; }
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pre { background: #1e1e2e; color: #cdd6f4; padding: 16px; border-radius: 6px; overflow-x: auto; font-size: 14px; }
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.output { background: #f0fdf4; border: 1px solid #bbf7d0; border-radius: 6px; padding: 12px; font-family: monospace; white-space: pre; margin-top: 10px; }
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a { color: #4f46e5; }
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</style>
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</head>
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<body>
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<p><a href="index.html">← tsb playground</a></p>
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<h1>📊 Variance &amp; Standard Error (sem_var)</h1>
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<p>
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<strong><code>varSeries</code></strong> / <strong><code>semSeries</code></strong> /
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<strong><code>varDataFrame</code></strong> / <strong><code>semDataFrame</code></strong> —
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compute sample/population variance and standard error of the mean, mirroring
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.var.html"><code>Series.var()</code></a> and
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.sem.html"><code>Series.sem()</code></a>.
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</p>
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<p>Equivalent Python: <code>series.var(ddof=1)</code> / <code>series.sem()</code></p>
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<h2>1 · Sample variance (ddof=1)</h2>
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<div class="demo">
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<pre>import { Series, varSeries } from "tsb";
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const s = new Series({ data: [2, 4, 4, 4, 5, 5, 7, 9] });
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varSeries(s); // 4.0 (sample variance, ddof=1)
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varSeries(s, { ddof: 0 }); // 3.5 (population variance, ddof=0)</pre>
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<div class="output">varSeries(s) → 4.0
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varSeries(s, {ddof:0}) → 3.5</div>
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</div>
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<h2>2 · Standard error of the mean</h2>
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<div class="demo">
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<pre>import { semSeries } from "tsb";
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// SEM = sqrt(var / n)
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semSeries(s); // sqrt(4 / 8) ≈ 0.7071</pre>
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<div class="output">semSeries(s) ≈ 0.7071</div>
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</div>
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<h2>3 · Handling missing values</h2>
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<div class="demo">
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<pre>const s2 = new Series({ data: [1, 2, 3, null, 5] });
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varSeries(s2); // skipna=true (default): ignores null
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varSeries(s2, { skipna: false }); // propagates NaN when null present
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varSeries(s2, { minCount: 5 }); // NaN: need 5 valid values but only 4</pre>
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<div class="output">varSeries(s2) → 2.9167 (approx)
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varSeries(s2, {skipna:false}) → NaN
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varSeries(s2, {minCount:5}) → NaN</div>
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</div>
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<h2>4 · DataFrame column-wise variance</h2>
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<div class="demo">
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<pre>import { DataFrame, varDataFrame, semDataFrame } from "tsb";
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const df = DataFrame.fromColumns({
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a: [1, 2, 3],
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b: [10, 20, 30],
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});
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varDataFrame(df); // Series { a: 1, b: 100 }
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semDataFrame(df); // Series { a: sqrt(1/3), b: sqrt(100/3) }
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varDataFrame(df, { axis: 1 }); // row-wise variance</pre>
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<div class="output">varDataFrame(df) → a: 1.0, b: 100.0
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semDataFrame(df) → a: ≈0.577, b: ≈5.774
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varDataFrame(df, {axis:1}) → row0: 20.25, row1: 81.0, row2: 182.25</div>
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</div>
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<h2>5 · numericOnly — skip non-numeric columns</h2>
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<div class="demo">
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<pre>const df2 = DataFrame.fromColumns({
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score: [10, 20, 30],
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label: ["A", "B", "C"],
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});
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varDataFrame(df2, { numericOnly: true });
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// Only includes "score", excludes "label"</pre>
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<div class="output">varDataFrame(df2, {numericOnly:true}) → score: 100.0</div>
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</div>
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</body>
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</html>

‎src/index.ts‎

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SkewKurtSeriesOptions,
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SkewKurtDataFrameOptions,
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} from "./stats/index.ts";
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export { varSeries, semSeries, varDataFrame, semDataFrame } from "./stats/index.ts";
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export type { VarSemSeriesOptions, VarSemDataFrameOptions } from "./stats/index.ts";
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export {
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nuniqueSeries,
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nuniqueDataFrame,
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anySeries,
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allSeries,
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anyDataFrame,
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allDataFrame,
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} from "./stats/index.ts";
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export type {
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NuniqueSeriesOptions,
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NuniqueDataFrameOptions,
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AnyAllSeriesOptions,
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AnyAllDataFrameOptions,
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} from "./stats/index.ts";

‎src/stats/index.ts‎

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SkewKurtSeriesOptions,
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SkewKurtDataFrameOptions,
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} from "./skew_kurt.ts";
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export { varSeries, semSeries, varDataFrame, semDataFrame } from "./sem_var.ts";
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export type { VarSemSeriesOptions, VarSemDataFrameOptions } from "./sem_var.ts";
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export {
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nuniqueSeries,
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nuniqueDataFrame,
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anySeries,
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allSeries,
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anyDataFrame,
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allDataFrame,
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} from "./nunique.ts";
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export type {
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NuniqueSeriesOptions,
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NuniqueDataFrameOptions,
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AnyAllSeriesOptions,
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AnyAllDataFrameOptions,
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} from "./nunique.ts";

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